Automated Machine Learning Model for the Heart Disease Prediction System
DOI:
https://doi.org/10.69996/fmep.2025029Keywords:
Heart Disease Prediction, Automated Machine Learning (AutoML), Random Forest, Support Vector Machine (SVM), Predictive AnalyticsAbstract
Early and accurate prediction methods are necessary to improve patient outcomes due to the fact that heart disease is still a significant cause of mortality globally. In this study, we introduce the Heart Disease Prediction System (HDPS), an AI-powered tool that uses clinical variables including age, gender, cholesterol, blood pressure, and heart rate to aid doctors in making accurate diagnoses of cardiac diseases. The medical datasets were analysed using a variety of machine learning methods, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours, and Random Forest. Out of all the algorithms, Random Forest achieved the best accuracy, reaching 90.5%. The system leverages automated model selection, performance evaluation, and feature optimization to reduce manual effort and enhance efficiency. Through rigorous experimentation and epoch-based analysis, the model demonstrated high reliability and consistency in prediction. The system’s user-friendly interface further promotes ease of use in clinical settings. Early diagnosis, personalised treatment planning, and enhanced patient care are all possible outcomes of incorporating AutoML into healthcare, as this study highlights. To further boost clinical value, future study will examine integration with explainable AI and real-time wearable data.
References
[1] S. Nandy, M. Adhikari, V. Balasubramanian, V.G. Menon, X. Li and M. Zakarya, “An intelligent heart disease prediction system based on swarm-artificial neural network,” Neural Computing and Applications, vol.35, no.20, pp.14723-14737, 2023.
[2] M. S. Al Reshan, S. Amin, M.A. Zeb, A. Sulaiman, H. Alshahrani and A. Shaikh, “A robust heart disease prediction system using hybrid deep neural networks,” IEEE Access, vol.11, pp.121574-121591, 2023.
[3] M.S. Al-Batah, M. S. Alzboon and R. Alazaidah, “Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals,” International Journal of Advanced Computer Science and Applications, vol.14, no.9, 2023.
[4] A. Rajdhan, A. Agarwal, M. Sai, D. Ravi and D.P. Ghuli, “Heart disease prediction using machine learning,” J. Eng. Sci, vol.14, no.4, pp.440-450, 2023.
[5] B.Venkateswarlu and Rekha Gangula, “Exploring the Power and Practical Applications of K-Nearest Neighbours (KNN) in Machine Learning,” Journal of Computer Allied Intelligence, vol.2, no.1, pp.8-15, 2024.
[6] A.A. Almazroi, E.A. Aldhahri, S. Bashir and S. Ashfaq, “A clinical decision support system for heart disease prediction using deep learning,” IEEE Access, vol.11, pp.61646- 61659, 2023.
[7] C. M. Bhatt, P. Patel, T. Ghetia and P.L. Mazzeo, “Effective heart disease prediction using machine learning techniques,” Algorithms, vol.16, no.2, pp.88, 2023.
[8] Swara Snehit Patil, “Artificial Intelligence: A Way to Promote Innovation,” Journal of Sensors, IoT and Health Sciences, vol.2, no.1, pp.1-5, 2024.
[9] W. A. W. A.Bakar, N. L. N. B. Josdi, M. B. Man and M. A. B. Zuhairi, “A review: Heart disease prediction in machine learning and deep learning,” In 2023 19th IEEE international colloquium on signal processing and its applications (CSPA), pp. 150-155, 2023.
[10] H. A. Al-Alshaikh, R.C. Poonia, A.K.J. Saudagar, M. Yadav, H.S. AlSagri and A.A. AlSanad, “Comprehensive evaluation and performance analysis of machine learning in heart disease prediction,” Scientific Reports, vol.14, no.1, pp.7819, 2024.
[11] A. Alali Diseph, Ojekudo Nathaniel and Egbono Frank Fubara, “Automated Information Retrieval System for Hospital Records Management,” International Journal of Advance Research and Innovation, vol.11, no.3, pp.7-10, 2023.
[12] K.B. Sk, D. Roja, S.S. Priya, L.Dalavi, S.S. Vellela and V. Reddy, “Coronary heart disease prediction and classification using hybrid machine learning algorithms,” In 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), pp. 1-7, 2023.
[13] N. Chandrasekhar and S. Peddakrishna, “Enhancing heart disease prediction accuracy through machine learning techniques and optimization,” Processes, vol.11, no.4, pp.1210, 2023.
[14] A. Jain, A.C.S. Rao, P.K. Jain and Y.C. Hu, “Optimized levy flight model for heart disease prediction using CNN framework in big data application,” Expert Systems with Applications, vol.223, pp.119859, 2023.
[15] S. Dhanka and S. Maini, “HyOPTXGBoost and HyOPTRF: hybridized intelligent systems using optuna optimization framework for heart disease prediction with clinical interpretations. Multimedia Tools and Applications, vol.83, no.29, pp.72889-72937, 2024.
[16] P. Dileep, K.N. Rao, P. Bodapati, S. Gokuruboyina, R. Peddi, A. Grover and A. Sheetal, “An automatic heart disease prediction using cluster-based bi-directional LSTM (CBiLSTM) algorithm,” Neural Computing and Applications, vol.35, no.10, pp.7253-7266, 2023.
[17] A.U. Rahman, Y. Alsenani, A. Zafar, K. Ullah, K. Rabie and T.Shongwe, “Enhancing heart disease prediction using a self-attention-based transformer model,” Scientific Reports, vol.14, no.1, pp.514, 2024.
[18] R. Ahmed, M. Bibi and S. Syed, “Improving heart disease prediction accuracy using a hybrid machine learning approach: A comparative study of SVM and KNN algorithms,” International Journal of Computations, Information and Manufacturing (IJCIM), vol.3, no.1, pp.49-54, 2023.
[19] G. Ramkumar, J. Seetha, R. Priyadarshini, M. Gopila and G.Saranya, “IoT-based patient monitoring system for predicting heart disease using deep learning,” Measurement, vol.218, pp.113235, 2023.
[20] R. R. Sarra, A. M. Dinar and M. A. Mohammed, “Enhanced accuracy for heart disease prediction using artificial neural network,” Indonesian Journal of Electrical Engineering and Computer Science, vol.29, no.1, pp.375-383, 2023.
[21] S.V. Babu, P. Ramya and J. Gracewell, “Revolutionizing heart disease prediction with quantum-enhanced machine learning,” Scientific Reports, vol.14, no.1, pp.7453, 2024.
[22] M.I. Hossain, M. H. Maruf, M. A.R. Khan, F.S. Prity, S. Fatema, M. S. Ejaz and M.A.S. Khan, “Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison,” Iran Journal of Computer Science, vol.6, no.4, pp.397-417, 2023.
[23] G. Rajkumar, T.G. Devi and A. Srinivasan, “Heart disease prediction using IoT based framework and improved deep learning approach: Medical application,” Medical engineering and physics, vol.111, 103937, 2023.
[24] Z. Noroozi, A. Orooji and L. Erfannia, “Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction,” Scientific reports, vol.13, no.1, pp.22588, 2023.
